Sonja Kallio (équipe Génie Civil et énergétique - GC-E- de l'unité de recherche ICube) soutient sa thèse intitulée Energy management of renewable energy-based micro-cogeneration systems in the context of smart grids, le mercredi 14 septembre 2022 à 10h en salle du conseil (D2.02). La soutenance est également accessible via le lien suivant : Click to follow the defence
Le jury de thèse est composé :
- de Monica Siroux, professeur – INSA Strasbourg, directrice de thèse
- des rapporteurs : Christophe Ménézo, professeur – Université Savoie-Mont-Blanc ; Patrick Salagnac, professeur – Université de la Rochelle ;
- de l’examinateur Rachid Bennacer, professeur – ENS Cachan Université Paris-Saclay
Résumé
Increasing energy demand and the ambitious primary energy and CO2 reduction targets of the European Union have increased interest in renewable energy-based micro-cogeneration systems that produce heat and power simultaneously from a single fuel source for on-site use of buildings.
Although these systems operate at high energy efficiency, deeper efficiency analysis is required to reveal hidden inefficiencies. The exergy analysis combines the first and second law of thermodynamics and is used to define, in addition to the quantity, the quality of energy supply. The exergy analysis has been mainly applied in industrial processes that are significantly different in nature compared to building energy systems.
However, this thesis aims to apply the exergy method to the micro-cogeneration and hybrid renewable energy systems on the building microgrids by performing energy, exergy and exergo-economic analysis, and exergy-based design optimization. This thesis highlights the importance of moving from the energy quantity-based evaluation of the building energy systems to the quality-based evaluation. The aim is to investigate the impact of different meteorological conditions on the results of the exergy analysis and to find out if the results differ from the energy analysis. Additionally, different energy management methods for the hybrid renewable energy system are studied by realizing an experimental set-up of a residential solar microgrid. The experimental set-up aims to collect the weather and PV production data to study different machine learning-based forecasting methods to be used in the advanced control methods.